Yang Jing, Penfold Christopher A, Grant Murray R, Rattray Magnus
Faculty of Life Sciences, University of Manchester, Manchester, UK.
Warwick Systems Biology Centre, University of Warwick, Coventry, UK.
Bioinformatics. 2016 Oct 1;32(19):2956-64. doi: 10.1093/bioinformatics/btw329. Epub 2016 Jun 10.
Time course data are often used to study the changes to a biological process after perturbation. Statistical methods have been developed to determine whether such a perturbation induces changes over time, e.g. comparing a perturbed and unperturbed time course dataset to uncover differences. However, existing methods do not provide a principled statistical approach to identify the specific time when the two time course datasets first begin to diverge after a perturbation; we call this the perturbation time. Estimation of the perturbation time for different variables in a biological process allows us to identify the sequence of events following a perturbation and therefore provides valuable insights into likely causal relationships.
We propose a Bayesian method to infer the perturbation time given time course data from a wild-type and perturbed system. We use a non-parametric approach based on Gaussian Process regression. We derive a probabilistic model of noise-corrupted and replicated time course data coming from the same profile before the perturbation time and diverging after the perturbation time. The likelihood function can be worked out exactly for this model and the posterior distribution of the perturbation time is obtained by a simple histogram approach, without recourse to complex approximate inference algorithms. We validate the method on simulated data and apply it to study the transcriptional change occurring in Arabidopsis following inoculation with Pseudomonas syringae pv. tomato DC3000 versus the disarmed strain DC3000hrpA AVAILABILITY AND IMPLEMENTATION: : An R package, DEtime, implementing the method is available at https://github.com/ManchesterBioinference/DEtime along with the data and code required to reproduce all the results.
Jing.Yang@manchester.ac.uk or Magnus.Rattray@manchester.ac.uk
Supplementary data are available at Bioinformatics online.
时间进程数据常用于研究扰动后生物过程的变化。已开发出统计方法来确定这种扰动是否随时间诱导变化，例如比较受扰动和未受扰动的时间进程数据集以发现差异。然而，现有方法未提供一种有原则的统计方法来确定两个时间进程数据集在扰动后首次开始出现差异的具体时间；我们将此称为扰动时间。估计生物过程中不同变量的扰动时间使我们能够识别扰动后的事件序列，从而为可能的因果关系提供有价值的见解。
我们提出一种贝叶斯方法，用于根据来自野生型和受扰动系统的时间进程数据推断扰动时间。我们使用基于高斯过程回归的非参数方法。我们推导了一个概率模型，该模型用于描述在扰动时间之前来自相同轮廓且在扰动时间之后发散的噪声干扰和重复的时间进程数据。对于该模型，可以精确计算似然函数，并且通过简单的直方图方法获得扰动时间的后验分布，而无需借助复杂的近似推断算法。我们在模拟数据上验证了该方法，并将其应用于研究拟南芥接种丁香假单胞菌番茄致病变种DC3000与无毒菌株DC3000hrpA后发生的转录变化。可用性与实现：一个实现该方法的R包DEtime可在https://github.com/ManchesterBioinference/DEtime获取，同时还提供了重现所有结果所需的数据和代码。
Jing.Yang@manchester.ac.uk或Magnus.Rattray@manchester.ac.uk
补充数据可在《生物信息学》在线获取。
